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layoutlm-funsd

Token Classification
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layoutlm
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  1. README.md +25 -25
  2. logs/events.out.tfevents.1709883660.533af087a84b.4340.0 +2 -2
  3. model.safetensors +1 -1
README.md CHANGED
@@ -17,14 +17,14 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.0307
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- - Answer: {'precision': 0.3855302279484638, 'recall': 0.48084054388133496, 'f1': 0.4279427942794279, 'number': 809}
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- - Header: {'precision': 0.34782608695652173, 'recall': 0.2689075630252101, 'f1': 0.3033175355450237, 'number': 119}
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- - Question: {'precision': 0.48268238761974946, 'recall': 0.6150234741784038, 'f1': 0.5408753096614369, 'number': 1065}
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- - Overall Precision: 0.4378
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- - Overall Recall: 0.5399
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- - Overall F1: 0.4835
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- - Overall Accuracy: 0.6393
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  ## Model description
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@@ -54,23 +54,23 @@ The following hyperparameters were used during training:
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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- |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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- | 1.7508 | 1.0 | 10 | 1.5163 | {'precision': 0.07105263157894737, 'recall': 0.10012360939431397, 'f1': 0.08311954848640328, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2490566037735849, 'recall': 0.18591549295774648, 'f1': 0.2129032258064516, 'number': 1065} | 0.1442 | 0.1400 | 0.1421 | 0.3638 |
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- | 1.4483 | 2.0 | 20 | 1.3842 | {'precision': 0.19585898153329603, 'recall': 0.4326328800988875, 'f1': 0.2696456086286595, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.27010309278350514, 'recall': 0.36901408450704226, 'f1': 0.3119047619047619, 'number': 1065} | 0.2286 | 0.3728 | 0.2834 | 0.4135 |
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- | 1.3068 | 3.0 | 30 | 1.2439 | {'precision': 0.2390092879256966, 'recall': 0.47713226205191595, 'f1': 0.3184818481848185, 'number': 809} | {'precision': 0.03125, 'recall': 0.01680672268907563, 'f1': 0.02185792349726776, 'number': 119} | {'precision': 0.32887189292543023, 'recall': 0.48450704225352115, 'f1': 0.39179954441913445, 'number': 1065} | 0.2783 | 0.4536 | 0.3450 | 0.4631 |
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- | 1.1868 | 4.0 | 40 | 1.1443 | {'precision': 0.25613802256138024, 'recall': 0.47713226205191595, 'f1': 0.33333333333333337, 'number': 809} | {'precision': 0.1797752808988764, 'recall': 0.13445378151260504, 'f1': 0.15384615384615385, 'number': 119} | {'precision': 0.3619233268356075, 'recall': 0.5230046948356808, 'f1': 0.42780337941628266, 'number': 1065} | 0.3059 | 0.4812 | 0.3740 | 0.5267 |
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- | 1.0837 | 5.0 | 50 | 1.1479 | {'precision': 0.27571728481455565, 'recall': 0.48702101359703337, 'f1': 0.3521000893655049, 'number': 809} | {'precision': 0.2696629213483146, 'recall': 0.20168067226890757, 'f1': 0.23076923076923078, 'number': 119} | {'precision': 0.3705616526791478, 'recall': 0.5389671361502347, 'f1': 0.4391736801836266, 'number': 1065} | 0.3234 | 0.4977 | 0.3921 | 0.5252 |
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- | 1.0102 | 6.0 | 60 | 1.1154 | {'precision': 0.29912810194500333, 'recall': 0.5512978986402967, 'f1': 0.3878260869565217, 'number': 809} | {'precision': 0.2604166666666667, 'recall': 0.21008403361344538, 'f1': 0.23255813953488375, 'number': 119} | {'precision': 0.44872918492550395, 'recall': 0.4807511737089202, 'f1': 0.4641885766092475, 'number': 1065} | 0.3603 | 0.4932 | 0.4164 | 0.5831 |
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- | 0.9349 | 7.0 | 70 | 1.0180 | {'precision': 0.3333333333333333, 'recall': 0.4289245982694685, 'f1': 0.37513513513513513, 'number': 809} | {'precision': 0.32558139534883723, 'recall': 0.23529411764705882, 'f1': 0.2731707317073171, 'number': 119} | {'precision': 0.42487046632124353, 'recall': 0.615962441314554, 'f1': 0.5028746646224608, 'number': 1065} | 0.3860 | 0.5173 | 0.4421 | 0.6121 |
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- | 0.8786 | 8.0 | 80 | 1.0198 | {'precision': 0.3177723177723178, 'recall': 0.4796044499381953, 'f1': 0.3822660098522168, 'number': 809} | {'precision': 0.2815533980582524, 'recall': 0.24369747899159663, 'f1': 0.26126126126126126, 'number': 119} | {'precision': 0.4321808510638298, 'recall': 0.6103286384976526, 'f1': 0.5060334760607241, 'number': 1065} | 0.3773 | 0.5354 | 0.4426 | 0.6088 |
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- | 0.8204 | 9.0 | 90 | 1.0123 | {'precision': 0.3665987780040733, 'recall': 0.44499381953028433, 'f1': 0.40201005025125625, 'number': 809} | {'precision': 0.2903225806451613, 'recall': 0.226890756302521, 'f1': 0.25471698113207547, 'number': 119} | {'precision': 0.45675482487491065, 'recall': 0.6, 'f1': 0.5186688311688312, 'number': 1065} | 0.4147 | 0.5148 | 0.4594 | 0.6320 |
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- | 0.8126 | 10.0 | 100 | 1.0461 | {'precision': 0.37877312560856863, 'recall': 0.48084054388133496, 'f1': 0.42374727668845313, 'number': 809} | {'precision': 0.3, 'recall': 0.226890756302521, 'f1': 0.25837320574162675, 'number': 119} | {'precision': 0.4764521193092622, 'recall': 0.5699530516431925, 'f1': 0.5190252244548953, 'number': 1065} | 0.4279 | 0.5133 | 0.4667 | 0.6288 |
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- | 0.7357 | 11.0 | 110 | 1.0160 | {'precision': 0.3771839671120247, 'recall': 0.453646477132262, 'f1': 0.4118967452300786, 'number': 809} | {'precision': 0.29357798165137616, 'recall': 0.2689075630252101, 'f1': 0.28070175438596495, 'number': 119} | {'precision': 0.4672639558924879, 'recall': 0.6366197183098592, 'f1': 0.5389507154213037, 'number': 1065} | 0.4252 | 0.5404 | 0.4759 | 0.6369 |
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- | 0.7249 | 12.0 | 120 | 1.0246 | {'precision': 0.38046795523906407, 'recall': 0.4622991347342398, 'f1': 0.4174107142857143, 'number': 809} | {'precision': 0.29411764705882354, 'recall': 0.25210084033613445, 'f1': 0.27149321266968324, 'number': 119} | {'precision': 0.4727403156384505, 'recall': 0.6187793427230047, 'f1': 0.5359902399349329, 'number': 1065} | 0.4288 | 0.5334 | 0.4754 | 0.6387 |
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- | 0.7015 | 13.0 | 130 | 1.0335 | {'precision': 0.36654135338345867, 'recall': 0.4820766378244747, 'f1': 0.416444207154298, 'number': 809} | {'precision': 0.31521739130434784, 'recall': 0.24369747899159663, 'f1': 0.27488151658767773, 'number': 119} | {'precision': 0.4788104089219331, 'recall': 0.6046948356807512, 'f1': 0.5344398340248964, 'number': 1065} | 0.4250 | 0.5334 | 0.4731 | 0.6326 |
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- | 0.6696 | 14.0 | 140 | 1.0364 | {'precision': 0.3841121495327103, 'recall': 0.5080346106304079, 'f1': 0.43746673762639704, 'number': 809} | {'precision': 0.32941176470588235, 'recall': 0.23529411764705882, 'f1': 0.2745098039215686, 'number': 119} | {'precision': 0.48804934464148036, 'recall': 0.5943661971830986, 'f1': 0.5359864521591872, 'number': 1065} | 0.4372 | 0.5379 | 0.4823 | 0.6394 |
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- | 0.6661 | 15.0 | 150 | 1.0307 | {'precision': 0.3855302279484638, 'recall': 0.48084054388133496, 'f1': 0.4279427942794279, 'number': 809} | {'precision': 0.34782608695652173, 'recall': 0.2689075630252101, 'f1': 0.3033175355450237, 'number': 119} | {'precision': 0.48268238761974946, 'recall': 0.6150234741784038, 'f1': 0.5408753096614369, 'number': 1065} | 0.4378 | 0.5399 | 0.4835 | 0.6393 |
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  ### Framework versions
 
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  This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.0339
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+ - Answer: {'precision': 0.4001766784452297, 'recall': 0.5599505562422744, 'f1': 0.46676970633693976, 'number': 809}
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+ - Header: {'precision': 0.3146067415730337, 'recall': 0.23529411764705882, 'f1': 0.2692307692307692, 'number': 119}
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+ - Question: {'precision': 0.5092221331194867, 'recall': 0.596244131455399, 'f1': 0.5493079584775085, 'number': 1065}
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+ - Overall Precision: 0.4522
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+ - Overall Recall: 0.5600
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+ - Overall F1: 0.5003
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+ - Overall Accuracy: 0.6347
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  ## Model description
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
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+ |:-------------:|:-----:|:----:|:---------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
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+ | 1.6941 | 1.0 | 10 | 1.4585 | {'precision': 0.09797822706065319, 'recall': 0.1557478368355995, 'f1': 0.12028639618138426, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2629193109700816, 'recall': 0.27230046948356806, 'f1': 0.26752767527675275, 'number': 1065} | 0.1741 | 0.2087 | 0.1899 | 0.3863 |
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+ | 1.3912 | 2.0 | 20 | 1.3157 | {'precision': 0.19625137816979052, 'recall': 0.4400494437577256, 'f1': 0.27144491040792984, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.2574061882817643, 'recall': 0.3671361502347418, 'f1': 0.3026315789473684, 'number': 1065} | 0.2231 | 0.3748 | 0.2797 | 0.4259 |
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+ | 1.2646 | 3.0 | 30 | 1.1981 | {'precision': 0.23537234042553193, 'recall': 0.43757725587144625, 'f1': 0.30609597924773024, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.35086633663366334, 'recall': 0.532394366197183, 'f1': 0.42297650130548303, 'number': 1065} | 0.2908 | 0.4621 | 0.3570 | 0.4979 |
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+ | 1.1512 | 4.0 | 40 | 1.0937 | {'precision': 0.2754578754578755, 'recall': 0.4647713226205192, 'f1': 0.3459061637534499, 'number': 809} | {'precision': 0.12048192771084337, 'recall': 0.08403361344537816, 'f1': 0.09900990099009901, 'number': 119} | {'precision': 0.3988563259471051, 'recall': 0.523943661971831, 'f1': 0.45292207792207795, 'number': 1065} | 0.3316 | 0.4737 | 0.3901 | 0.5719 |
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+ | 1.052 | 5.0 | 50 | 1.0996 | {'precision': 0.2841163310961969, 'recall': 0.47095179233621753, 'f1': 0.35441860465116287, 'number': 809} | {'precision': 0.23529411764705882, 'recall': 0.13445378151260504, 'f1': 0.17112299465240638, 'number': 119} | {'precision': 0.40622929092113985, 'recall': 0.5755868544600939, 'f1': 0.47630147630147635, 'number': 1065} | 0.3461 | 0.5068 | 0.4113 | 0.5719 |
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+ | 0.9901 | 6.0 | 60 | 1.0590 | {'precision': 0.3064992614475628, 'recall': 0.5129789864029666, 'f1': 0.3837263060564031, 'number': 809} | {'precision': 0.2345679012345679, 'recall': 0.15966386554621848, 'f1': 0.18999999999999997, 'number': 119} | {'precision': 0.4610441767068273, 'recall': 0.5389671361502347, 'f1': 0.496969696969697, 'number': 1065} | 0.3761 | 0.5058 | 0.4314 | 0.6011 |
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+ | 0.9158 | 7.0 | 70 | 1.0134 | {'precision': 0.3295238095238095, 'recall': 0.4276885043263288, 'f1': 0.3722431414739107, 'number': 809} | {'precision': 0.26506024096385544, 'recall': 0.18487394957983194, 'f1': 0.21782178217821785, 'number': 119} | {'precision': 0.45186226282501757, 'recall': 0.603755868544601, 'f1': 0.5168810289389068, 'number': 1065} | 0.3955 | 0.5073 | 0.4445 | 0.6314 |
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+ | 0.8626 | 8.0 | 80 | 1.0097 | {'precision': 0.3275862068965517, 'recall': 0.46971569839307786, 'f1': 0.3859827323514474, 'number': 809} | {'precision': 0.3157894736842105, 'recall': 0.20168067226890757, 'f1': 0.24615384615384614, 'number': 119} | {'precision': 0.44047619047619047, 'recall': 0.6253521126760564, 'f1': 0.5168800931315483, 'number': 1065} | 0.3894 | 0.5369 | 0.4514 | 0.6276 |
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+ | 0.8026 | 9.0 | 90 | 1.0030 | {'precision': 0.372310570626754, 'recall': 0.4919653893695921, 'f1': 0.42385516506922255, 'number': 809} | {'precision': 0.2736842105263158, 'recall': 0.2184873949579832, 'f1': 0.2429906542056075, 'number': 119} | {'precision': 0.49289454001495886, 'recall': 0.6187793427230047, 'f1': 0.5487094088259784, 'number': 1065} | 0.4330 | 0.5434 | 0.4820 | 0.6410 |
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+ | 0.794 | 10.0 | 100 | 1.0143 | {'precision': 0.3772893772893773, 'recall': 0.5092707045735476, 'f1': 0.4334560757496055, 'number': 809} | {'precision': 0.2857142857142857, 'recall': 0.20168067226890757, 'f1': 0.23645320197044337, 'number': 119} | {'precision': 0.4923572003218021, 'recall': 0.5746478873239437, 'f1': 0.5303292894280762, 'number': 1065} | 0.4332 | 0.5258 | 0.4751 | 0.6380 |
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+ | 0.7156 | 11.0 | 110 | 1.0071 | {'precision': 0.38151875571820676, 'recall': 0.515451174289246, 'f1': 0.43848580441640383, 'number': 809} | {'precision': 0.2828282828282828, 'recall': 0.23529411764705882, 'f1': 0.25688073394495414, 'number': 119} | {'precision': 0.5, 'recall': 0.6131455399061033, 'f1': 0.5508224377899621, 'number': 1065} | 0.4396 | 0.5509 | 0.4890 | 0.6393 |
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+ | 0.7015 | 12.0 | 120 | 1.0361 | {'precision': 0.3828867761452031, 'recall': 0.5475896168108776, 'f1': 0.45066124109867756, 'number': 809} | {'precision': 0.3111111111111111, 'recall': 0.23529411764705882, 'f1': 0.2679425837320574, 'number': 119} | {'precision': 0.49387442572741197, 'recall': 0.6056338028169014, 'f1': 0.5440742302825812, 'number': 1065} | 0.4371 | 0.5600 | 0.4910 | 0.6326 |
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+ | 0.681 | 13.0 | 130 | 1.0591 | {'precision': 0.38740293356341676, 'recall': 0.5550061804697157, 'f1': 0.4563008130081301, 'number': 809} | {'precision': 0.345679012345679, 'recall': 0.23529411764705882, 'f1': 0.27999999999999997, 'number': 119} | {'precision': 0.5167074164629177, 'recall': 0.5953051643192488, 'f1': 0.5532286212914486, 'number': 1065} | 0.4503 | 0.5575 | 0.4982 | 0.6299 |
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+ | 0.6461 | 14.0 | 140 | 1.0191 | {'precision': 0.38854625550660793, 'recall': 0.5451174289245982, 'f1': 0.45370370370370366, 'number': 809} | {'precision': 0.3333333333333333, 'recall': 0.23529411764705882, 'f1': 0.27586206896551724, 'number': 119} | {'precision': 0.49961330239752516, 'recall': 0.6065727699530516, 'f1': 0.547921967769296, 'number': 1065} | 0.4439 | 0.5595 | 0.4950 | 0.6351 |
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+ | 0.6518 | 15.0 | 150 | 1.0339 | {'precision': 0.4001766784452297, 'recall': 0.5599505562422744, 'f1': 0.46676970633693976, 'number': 809} | {'precision': 0.3146067415730337, 'recall': 0.23529411764705882, 'f1': 0.2692307692307692, 'number': 119} | {'precision': 0.5092221331194867, 'recall': 0.596244131455399, 'f1': 0.5493079584775085, 'number': 1065} | 0.4522 | 0.5600 | 0.5003 | 0.6347 |
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  ### Framework versions
logs/events.out.tfevents.1709883660.533af087a84b.4340.0 CHANGED
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